Byte-Sized-Chunks: Recommendation Systems

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Byte-Sized-Chunks: Recommendation Systems. Build a movie recommendation system in Python – master both theory and practice

20,00  10,00 

The name of this course is Byte-Sized-Chunks: Recommendation Systems. The knowledge you will get with this indescribable online course is astonishing. Build a movie recommendation system in Python – master both theory and practice.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Loony Corn, one of the very best experts in this field.

Description of this course: Byte-Sized-Chunks: Recommendation Systems

Course Description Note: This course is a subset of our 20+ hour course ‘From 0 to 1: Machine Learning & Natural Language Processing’ so please don’t sign up for both:-)Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.Recommendation Engines perform a variety of tasks – but the most important one is to find products that are most relevant to the user. Content based filtering finds products relevant to a user – based on the content of the product (attributes, description, words etc).Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to RecommendationsNeighborhood models – also known as Memory based approaches – rely on finding users similar to the active user. Similarity can be measured in many ways – Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.Recommendation Systems in Python!Movielens is a famous dataset with movie ratings. Use Pandas to read and play around with the data.Also learn how to use Scipy and Numpy

Requirements of this course: Byte-Sized-Chunks: Recommendation Systems

What are the requirements? No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

What will you learn in this course: Byte-Sized-Chunks: Recommendation Systems?

What am I going to get from this course? Identify use-cases for recommendation systems Design and Implement recommendation systems in Python Understand the theory underlying this important technique in machine learning

Target audience of this course: Byte-Sized-Chunks: Recommendation Systems

Who is the target audience? Nope! Please don’t enroll for this class if you have already enrolled for our 21-hour course ‘From 0 to 1: Machine Learning and NLP in Python’ Yep! Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role